How Many Deaths Can Be Prevented by Newborn Screening for Congenital Adrenal Hyperplasia?
Bibliographic record
Abstract
BACKGROUND/AIMS: Congenital adrenal hyperplasia (CAH) is increasingly being included in newborn screening programs. Screening can prevent neonatal mortality in children with salt-wasting CAH, but the number of deaths prevented is not known. Cost-effectiveness analyses of screening require estimates of the probability of mortality in CAH. METHODS: We reviewed the literature to identify cohort studies of children with CAH ascertained clinically in the absence of screening. We abstracted the numbers of infant deaths attributable to CAH. We also addressed sex ratios among children with clinically detected CAH and the contribution of ascertainment bias to unbalanced ratios. RESULTS: The evidence suggests a probability of infant death due to adrenal crises in salt-wasting CAH of 4% or less in contemporary advanced economies without screening for CAH. This is lower than previous estimates, although the rate of mortality could be considerably higher in populations with limited clinical awareness or access. CONCLUSION: Although screening for CAH is conducted in a number of countries, further research is still needed to provide reliable estimates on the numbers of prevented deaths, along with evidence-based assessments of the potential benefits, harms, and costs of screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".